Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Calibrating land surface phenology (LSP) with tree rings is important to model spatio-temporal variations in forest productivity. We used MODIS (resolution: 250 m) NDVI, WDRVI and EVI series 2000-2014 to derive LSP metrics quantifying phenophase timing and canopy photosynthetic rates of 26 European beech forests covering a large thermal gradient (5-16 °C) in Italy. Average phenophase timing changed greatly with site temperature (e.g. growing season 70 days longer at low- than high-elevation); average VI values were affected by precipitation. An annual temperature about 12 °C (c. 1100 m asl) represented a bioclimatic threshold dividing warm from cold beech forests, distinguished by different phenology-BAI (basal area increment) relationships and LSP trends. Cold forests showed decreasing VI values (browning) and delayed phenophases and had negative BAI slopes. Warmer forests tended to increase VI (greening), and positive BAI slopes. NDVI peak, commonly used in global trend assessments, changed with elevation in agreement with changes in wood production. A cross-validation modelling approach demonstrated the ability of LSP to predict average BAI and its interannual variability. Merging sites into bioclimatic groups improved models by amplifying the signal in growth or LSP. NDVI had highest performances when informing on BAI trends; WDRVI and EVI were mostly selected for modelling mean and interannual BAI. WDRVI association with tree rings, tested in this study for the first time, showed that this VI is highly promising for studying forest dynamics. MODIS LSP can quantify forest functioning changes across landscapes and model interannual spatial variations and trends in productivity dynamics under climate change.
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Source |
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http://dx.doi.org/10.1007/s00484-022-02367-2 | DOI Listing |
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